Tianzhong Yang

The paper introduces pt-TWAS, a framework that identifies disease genes along cell developmental paths by modeling gene expression as a continuous function of pseudotime, improving resolution over discrete cell-type analyses.

This study identifies 14 susceptibility loci for Ewing sarcoma through a genome-wide association meta-analysis, revealing genetic interactions with somatic EWSR1::FLI1 fusions.

This paper introduces two new variance-based causal measures and a mixed-effects estimation procedure to accurately quantify weak mediation effects in high-dimensional omics data.

The paper introduces an R2 total mediation effect measure for binary traits in case-control studies with high-dimensional mediators, showing 89% of BMI's effect on CHD is mediated by metabolomics.

The paper infers a directed acyclic graph of phenotypes from GWAS summary statistics and applies it to cardiovascular proteins linked to Alzheimer's disease.

TraitScan identifies relevant subsets of traits from large genetic datasets, improving multi-trait analysis power and outperforming existing methods in simulations and real-world application to Ewing Sarcoma.

The paper introduces a cross-fitted estimation procedure for R2-based mediation effects in high-dimensional mediators, showing improved computational efficiency while maintaining statistical accuracy.

The paper extends a mediation effect measure to high-dimensional omics data and applies it to the Framingham Heart Study, finding that gene expression explains over half of aging-related systolic blood pressure variation.